• Why do we segment?
• When it is mostly important?

A Definition:

 Market Segmentation is concerned with individual or intergroup differences in response to marketing mix variables. The managerial presumption is that if these response differences exist, can be identified, are reasonably stable over time and the segments can be efficiently reached the firm may increase its sales and profits beyond those obtained by assuming market homogeneity.

Du-Pont’s Definition:

 “A group of customers anywhere along the distribution chain who have common needs and values - who will respond similarly to our offerings and who are large enough to be strategically important to our business.”

Cluster Analysis:

 C.A is a set of techniques which Classify, based on observed characteristics, an heterogeneous aggregate of people, objects or variables, into more homogeneous groups.C.A is useful to identify market segments, competitors in market structure analysis, matched cities in test market etc.

Cluster analysis is widely used in market research when working with multivariate data from surveys and test panels. Market researchers use cluster analysis to partition the general population of consumers into market segments and to better understand the relationships between different groups of consumers/potential customers.

• Segmenting the market and determining target markets
• Product positioning
• New product development
• Selecting test markets (see : experimental techniques)

Hierarchical clusters  

are nested tree-like structures, and usually reflect a development sequence. Each person, product or occasion is treated as a separate and distinct cluster to begin with. They are merged using an appropriate similarity measure until every object belongs to a large cluster. It may help for “seeing the market structure” in terms of brands. For a set of 100 persons the H.C.A will start with 100 clusters, each containing 1 object and finish with 1 cluster.

Non-hierarchical methods  

cluster a data set into a single classification of a number of clusters fewer than the number of objects. The number of the cluster may be specified a-priori or determined as part of the clustering method